Deep learning algorithms, often critiqued for their 'black box' nature,
traditionally fall short in providing the necessary transparency for trusted
clinical use. This challenge is particularly evident when such models are
deployed in local hospitals, encountering out-of-domain distributions due to
varying imaging techniques and patient-specific pathologies. Yet, this
limitation offers a unique avenue for continual learning. The
Uncertainty-Guided Annotation (UGA) framework introduces a human-in-the-loop
approach, enabling AI to convey its uncertainties to clinicians, effectively
acting as an automated quality control mechanism. UGA eases this interaction by
quantifying uncertainty at the pixel level, thereby revealing the model's
limitations and opening the door for clinician-guided corrections. We evaluated
UGA on the Camelyon dataset for lymph node metastasis segmentation which
revealed that UGA improved the Dice coefficient (DC), from 0.66 to 0.76 by
adding 5 patches, and further to 0.84 with 10 patches. To foster broader
application and community contribution, we have made our code accessible at

为解决深度学习算法在临床应用中缺乏透明度的问题，研究提出了一种基于不确定性引导标注的框架，通过量化不确定性和开放临床人员指导，实现自动质量控制，提高算法性能。