diff --git a/dask_expr/_merge.py b/dask_expr/_merge.py
index 8c8f898b8..01a1c9e3d 100644
--- a/dask_expr/_merge.py
+++ b/dask_expr/_merge.py
@@ -428,52 +428,56 @@ def _layer(self) -> dict:
         from distributed.shuffle._shuffle import shuffle_barrier
 
         dsk = {}
-        name_left = "hash-join-transfer-" + self.left._name
-        name_right = "hash-join-transfer-" + self.right._name
+        token = self._name.split("-")[-1]
+        token_left = token + "-left"
+        token_right = token + "-right"
+        _barrier_key_left = barrier_key(ShuffleId(token_left))
+        _barrier_key_right = barrier_key(ShuffleId(token_right))
+
+        transfer_name_left = "hash-join-transfer-" + token_left
+        transfer_name_right = "hash-join-transfer-" + token_right
         transfer_keys_left = list()
         transfer_keys_right = list()
         func = create_assign_index_merge_transfer()
         for i in range(self.left.npartitions):
-            transfer_keys_left.append((name_left, i))
-            dsk[(name_left, i)] = (
+            transfer_keys_left.append((transfer_name_left, i))
+            dsk[(transfer_name_left, i)] = (
                 func,
                 (self.left._name, i),
                 self.shuffle_left_on,
                 _HASH_COLUMN_NAME,
                 self.npartitions,
-                self.left._name,
+                token_left,
                 i,
                 self.left._meta,
                 self._partitions,
             )
         for i in range(self.right.npartitions):
-            transfer_keys_right.append((name_right, i))
-            dsk[(name_right, i)] = (
+            transfer_keys_right.append((transfer_name_right, i))
+            dsk[(transfer_name_right, i)] = (
                 func,
                 (self.right._name, i),
                 self.shuffle_right_on,
                 _HASH_COLUMN_NAME,
                 self.npartitions,
-                self.right._name,
+                token_right,
                 i,
                 self.right._meta,
                 self._partitions,
             )
 
-        _barrier_key_left = barrier_key(ShuffleId(self.left._name))
-        _barrier_key_right = barrier_key(ShuffleId(self.right._name))
-        dsk[_barrier_key_left] = (shuffle_barrier, self.left._name, transfer_keys_left)
+        dsk[_barrier_key_left] = (shuffle_barrier, token_left, transfer_keys_left)
         dsk[_barrier_key_right] = (
             shuffle_barrier,
-            self.right._name,
+            token_right,
             transfer_keys_right,
         )
 
         for part_out in self._partitions:
             dsk[(self._name, part_out)] = (
                 merge_unpack,
-                self.left._name,
-                self.right._name,
+                token_left,
+                token_right,
                 part_out,
                 _barrier_key_left,
                 _barrier_key_right,
diff --git a/dask_expr/tests/test_distributed.py b/dask_expr/tests/test_distributed.py
index e83836eca..4bd59860a 100644
--- a/dask_expr/tests/test_distributed.py
+++ b/dask_expr/tests/test_distributed.py
@@ -8,6 +8,7 @@
 distributed = pytest.importorskip("distributed")
 
 from distributed import Client, LocalCluster
+from distributed.shuffle._core import id_from_key
 from distributed.utils_test import client as c  # noqa F401
 from distributed.utils_test import gen_cluster
 
@@ -55,6 +56,46 @@ async def test_merge_p2p_shuffle(c, s, a, b, npartitions_left):
     lib.testing.assert_frame_equal(x.reset_index(drop=True), df_left.merge(df_right))
 
 
+@gen_cluster(client=True)
+async def test_self_merge_p2p_shuffle(c, s, a, b):
+    pdf = lib.DataFrame({"a": range(100), "b": range(0, 200, 2)})
+    ddf = from_pandas(pdf, npartitions=5)
+
+    out = ddf.merge(ddf, left_on="a", right_on="b", shuffle_backend="p2p")
+    # Generate unique shuffle IDs if the input frame is the same but parameters differ
+    assert sum(id_from_key(k) is not None for k in out.dask) == 2
+    x = await c.compute(out)
+    expected = pdf.merge(pdf, left_on="a", right_on="b")
+    lib.testing.assert_frame_equal(
+        x.sort_values("a_x", ignore_index=True),
+        expected.sort_values("a_x", ignore_index=True),
+    )
+
+
+@gen_cluster(client=True)
+async def test_merge_p2p_shuffle_reused_dataframe(c, s, a, b):
+    pdf1 = lib.DataFrame({"a": range(100), "b": range(0, 200, 2)})
+    pdf2 = lib.DataFrame({"x": range(200), "y": [1, 2, 3, 4] * 50})
+    ddf1 = from_pandas(pdf1, npartitions=5)
+    ddf2 = from_pandas(pdf2, npartitions=10)
+
+    out = (
+        ddf1.merge(ddf2, left_on="a", right_on="x", shuffle_backend="p2p")
+        .repartition(20)
+        .merge(ddf2, left_on="b", right_on="x", shuffle_backend="p2p")
+    )
+    # Generate unique shuffle IDs if the input frame is the same but parameters differ
+    assert sum(id_from_key(k) is not None for k in out.dask) == 4
+    x = await c.compute(out)
+    expected = pdf1.merge(pdf2, left_on="a", right_on="x").merge(
+        pdf2, left_on="b", right_on="x"
+    )
+    lib.testing.assert_frame_equal(
+        x.sort_values("a", ignore_index=True),
+        expected.sort_values("a", ignore_index=True),
+    )
+
+
 @pytest.mark.parametrize("npartitions_left", [5, 6])
 @gen_cluster(client=True)
 async def test_index_merge_p2p_shuffle(c, s, a, b, npartitions_left):