  Segmentation quality assessment (SQA) plays a critical role in the deployment
of a medical image based AI system. Users need to be informed/alerted whenever
an AI system generates unreliable/incorrect predictions. With the introduction
of the Segment Anything Model (SAM), a general foundation segmentation model,
new research opportunities emerged in how one can utilize SAM for medical image
segmentation. In this paper, we propose a novel SQA method, called SQA-SAM,
which exploits SAM to enhance the accuracy of quality assessment for medical
image segmentation. When a medical image segmentation model (MedSeg) produces
predictions for a test image, we generate visual prompts based on the
predictions, and SAM is utilized to generate segmentation maps corresponding to
the visual prompts. How well MedSeg's segmentation aligns with SAM's
segmentation indicates how well MedSeg's segmentation aligns with the general
perception of objectness and image region partition. We develop a score measure
for such alignment. In experiments, we find that the generated scores exhibit
moderate to strong positive correlation (in Pearson correlation and Spearman
correlation) with Dice coefficient scores reflecting the true segmentation
quality.
