  Performance of deep learning segmentation models is significantly challenged
in its transferability across different medical imaging domains, particularly
when aiming to adapt these models to a target domain with insufficient
annotated data for effective fine-tuning. While existing domain adaptation (DA)
methods propose strategies to alleviate this problem, these methods do not
explicitly incorporate human-verified segmentation priors, compromising the
potential of a model to produce anatomically plausible segmentations. We
introduce RL4Seg, an innovative reinforcement learning framework that reduces
the need to otherwise incorporate large expertly annotated datasets in the
target domain, and eliminates the need for lengthy manual human review. Using a
target dataset of 10,000 unannotated 2D echocardiographic images, RL4Seg not
only outperforms existing state-of-the-art DA methods in accuracy but also
achieves 99% anatomical validity on a subset of 220 expert-validated subjects
from the target domain. Furthermore, our framework's reward network offers
uncertainty estimates comparable with dedicated state-of-the-art uncertainty
methods, demonstrating the utility and effectiveness of RL4Seg in overcoming
domain adaptation challenges in medical image segmentation.
