  Deep learning-based techniques have proven effective in polyp segmentation
tasks when provided with sufficient pixel-wise labeled data. However, the high
cost of manual annotation has created a bottleneck for model generalization. To
minimize annotation costs, we propose a deep active learning framework for
annotation-efficient polyp segmentation. In practice, we measure the
uncertainty of each sample by examining the similarity between features masked
by the prediction map of the polyp and the background area. Since the
segmentation model tends to perform weak in samples with indistinguishable
features of foreground and background areas, uncertainty sampling facilitates
the fitting of under-learning data. Furthermore, clustering image-level
features weighted by uncertainty identify samples that are both uncertain and
representative. To enhance the selectivity of the active selection strategy, we
propose a novel unsupervised feature discrepancy learning mechanism. The
selection strategy and feature optimization work in tandem to achieve optimal
performance with a limited annotation budget. Extensive experimental results
have demonstrated that our proposed method achieved state-of-the-art
performance compared to other competitors on both a public dataset and a
large-scale in-house dataset.
