diff --git a/cleanlab/pruning.py b/cleanlab/pruning.py
index 63e4a377dc..cc30f3a8a1 100644
--- a/cleanlab/pruning.py
+++ b/cleanlab/pruning.py
@@ -29,6 +29,7 @@
 from sklearn.preprocessing import MultiLabelBinarizer
 import multiprocessing
 from multiprocessing.sharedctypes import RawArray
+from contextlib import contextmanager
 import sys
 import os
 import time
@@ -56,19 +57,19 @@
 # pruning, regardless if noise estimates are larger.
 MIN_NUM_PER_CLASS = 5
 
-# For python 2/3 compatibility, define pool context manager
-# to support the 'with' statement in Python 2
-if sys.version_info[0] == 2:
-    from contextlib import contextmanager
-
-
-    @contextmanager
-    def multiprocessing_context(*args, **kwargs):
-        pool = multiprocessing.Pool(*args, **kwargs)
-        yield pool
-        pool.terminate()
-else:
-    multiprocessing_context = multiprocessing.Pool
+# Coverage testing registers finalizers that don't run if
+# `multiprocessing.Pool.terminate()` is called
+# (`multiprocessing.Pool.__exit__()` also calls `terminate()`), so we use this
+# alternative context manager that calls `join()` to ensure finalizers are run
+# reliably.
+#
+# See https://pytest-cov.readthedocs.io/en/latest/subprocess-support.html#if-you-use-multiprocessing-pool
+@contextmanager
+def joining_pool(*args, **kwargs):
+    pool = multiprocessing.Pool(*args, **kwargs)
+    yield pool
+    pool.close()
+    pool.join()
 
 # Globals to be shared across threads in multiprocessing
 mp_params = {}  # parameters passed to multiprocessing helper functions
@@ -392,7 +393,7 @@ def get_noise_indices(
     # Operations are parallelized across all CPU processes
     if prune_method == 'prune_by_class' or prune_method == 'both':
         if n_jobs > 1:  # parallelize
-            with multiprocessing_context(
+            with joining_pool(
                     n_jobs,
                     initializer=_init,
                     initargs=(_s, _s_counts, _prune_count_matrix,
@@ -417,7 +418,7 @@ def get_noise_indices(
 
     if prune_method == 'prune_by_noise_rate' or prune_method == 'both':
         if n_jobs > 1:  # parallelize
-            with multiprocessing_context(
+            with joining_pool(
                     n_jobs,
                     initializer=_init,
                     initargs=(_s, _s_counts, _prune_count_matrix,