  Deep learning-based medical image segmentation models often face performance
degradation when deployed across various medical centers, largely due to the
discrepancies in data distribution. Test Time Adaptation (TTA) methods, which
adapt pre-trained models to test data, have been employed to mitigate such
discrepancies. However, existing TTA methods primarily focus on manipulating
Batch Normalization (BN) layers or employing prompt and adversarial learning,
which may not effectively rectify the inconsistencies arising from divergent
data distributions. In this paper, we propose a novel Human-in-the-loop TTA
(HiTTA) framework that stands out in two significant ways. First, it
capitalizes on the largely overlooked potential of clinician-corrected
predictions, integrating these corrections into the TTA process to steer the
model towards predictions that coincide more closely with clinical annotation
preferences. Second, our framework conceives a divergence loss, designed
specifically to diminish the prediction divergence instigated by domain
disparities, through the careful calibration of BN parameters. Our HiTTA is
distinguished by its dual-faceted capability to acclimatize to the distribution
of test data whilst ensuring the model's predictions align with clinical
expectations, thereby enhancing its relevance in a medical context. Extensive
experiments on a public dataset underscore the superiority of our HiTTA over
existing TTA methods, emphasizing the advantages of integrating human feedback
and our divergence loss in enhancing the model's performance and adaptability
across diverse medical centers.
