diff --git a/monai/metrics/hausdorff_distance.py b/monai/metrics/hausdorff_distance.py
index d9bbf17db3..d727eb0567 100644
--- a/monai/metrics/hausdorff_distance.py
+++ b/monai/metrics/hausdorff_distance.py
@@ -190,7 +190,7 @@ def compute_hausdorff_distance(
             y[b, c],
             distance_metric=distance_metric,
             spacing=spacing_list[b],
-            symetric=not directed,
+            symmetric=not directed,
             class_index=c,
         )
         percentile_distances = [_compute_percentile_hausdorff_distance(d, percentile) for d in distances]
diff --git a/monai/metrics/surface_dice.py b/monai/metrics/surface_dice.py
index 635eb1bc24..b20b47a1a5 100644
--- a/monai/metrics/surface_dice.py
+++ b/monai/metrics/surface_dice.py
@@ -253,7 +253,7 @@ def compute_surface_dice(
             distance_metric=distance_metric,
             spacing=spacing_list[b],
             use_subvoxels=use_subvoxels,
-            symetric=True,
+            symmetric=True,
             class_index=c,
         )
         boundary_correct: int | torch.Tensor | float
diff --git a/monai/metrics/surface_distance.py b/monai/metrics/surface_distance.py
index 7ce632c588..3cb336d6a0 100644
--- a/monai/metrics/surface_distance.py
+++ b/monai/metrics/surface_distance.py
@@ -177,7 +177,7 @@ def compute_average_surface_distance(
             y[b, c],
             distance_metric=distance_metric,
             spacing=spacing_list[b],
-            symetric=symmetric,
+            symmetric=symmetric,
             class_index=c,
         )
         surface_distance = torch.cat(distances)
diff --git a/monai/metrics/utils.py b/monai/metrics/utils.py
index 62e6520b96..d4b8f6e9b6 100644
--- a/monai/metrics/utils.py
+++ b/monai/metrics/utils.py
@@ -295,7 +295,7 @@ def get_edge_surface_distance(
     distance_metric: str = "euclidean",
     spacing: int | float | np.ndarray | Sequence[int | float] | None = None,
     use_subvoxels: bool = False,
-    symetric: bool = False,
+    symmetric: bool = False,
     class_index: int = -1,
 ) -> tuple[
     tuple[torch.Tensor, torch.Tensor],
@@ -314,7 +314,7 @@ def get_edge_surface_distance(
             See :py:func:`monai.metrics.utils.get_surface_distance`.
         use_subvoxels: whether to use subvoxel resolution (using the spacing).
             This will return the areas of the edges.
-        symetric: whether to compute the surface distance from `y_pred` to `y` and from `y` to `y_pred`.
+        symmetric: whether to compute the surface distance from `y_pred` to `y` and from `y` to `y_pred`.
         class_index: The class-index used for context when warning about empty ground truth or prediction.
 
     Returns:
@@ -338,7 +338,7 @@ def get_edge_surface_distance(
             " this may result in nan/inf distance."
         )
     distances: tuple[torch.Tensor, torch.Tensor] | tuple[torch.Tensor]
-    if symetric:
+    if symmetric:
         distances = (
             get_surface_distance(edges_pred, edges_gt, distance_metric, spacing),
             get_surface_distance(edges_gt, edges_pred, distance_metric, spacing),