Breaking the Barrier: Selective Uncertainty-based Active Learning for
  Medical Image Segmentation
Siteng Ma Haochang Wu Aonghus Lawlor Ruihai Dong
Active learning (AL) has found wide applications in medical image
segmentation, aiming to alleviate the annotation workload and enhance
performance. Conventional uncertainty-based AL methods, such as entropy and
Bayesian, often rely on an aggregate of all pixel-level metrics. However, in
imbalanced settings, these methods tend to neglect the significance of target
regions, eg., lesions, and tumors. Moreover, uncertainty-based selection
introduces redundancy. These factors lead to unsatisfactory performance, and in
many cases, even underperform random sampling. To solve this problem, we
introduce a novel approach called the Selective Uncertainty-based AL, avoiding
the conventional practice of summing up the metrics of all pixels. Through a
filtering process, our strategy prioritizes pixels within target areas and
those near decision boundaries. This resolves the aforementioned disregard for
target areas and redundancy. Our method showed substantial improvements across
five different uncertainty-based methods and two distinct datasets, utilizing
fewer labeled data to reach the supervised baseline and consistently achieving
the highest overall performance. Our code is available at
https://github.com/HelenMa9998/Selective\_Uncertainty\_AL.