Active learning is considered a viable solution to alleviate the contradiction between the high dependency of deep learning-based segmentation methods on annotated data and the expensive pixel-level annotation cost of medical images. However, most existing methods suffer from unreliable uncertainty assessment and the struggle to balance diversity and informativeness, leading to poor performance in segmentation tasks. In response, we propose an efficient Predictive Accuracy-based Active Learning (PAAL) method for medical image segmentation, first introducing predictive accuracy to define uncertainty. Specifically, PAAL mainly consists of an Accuracy Predictor (AP) and a Weighted Polling Strategy (WPS). The former is an attached learnable module that can accurately predict the segmentation accuracy of unlabeled samples relative to the target model with the predicted posterior probability. The latter provides an efficient hybrid querying scheme by combining predicted accuracy and feature representation, aiming to ensure the uncertainty and diversity of the acquired samples. Extensive experiment results on multiple datasets demonstrate the superiority of PAAL. PAAL achieves comparable accuracy to fully annotated data while reducing annotation costs by approximately 50% to 80%, showcasing significant potential in clinical applications. The code is available at https://github.com/shijun18/PAAL-MedSeg.

翻译：主动学习被视为缓解基于深度学习的医学图像分割方法对标注数据的高度依赖与医学图像像素级标注成本高昂之间矛盾的有效方案。然而，现有方法大多存在不确定性评估不可靠以及难以平衡多样性与信息性的问题，导致在分割任务中表现不佳。为此，我们提出一种高效的基于预测精度的医学图像分割主动学习方法（PAAL），首次引入预测精度来定义不确定性。具体而言，PAAL主要由精度预测器（AP）与加权投票策略（WPS）构成。前者是一个附加的可学习模块，能够利用预测的后验概率，准确预测未标注样本相对于目标模型的分割精度。后者通过结合预测精度与特征表示，提供一种高效的混合查询方案，旨在确保所获取样本的不确定性与多样性。在多个数据集上的大量实验结果证明了PAAL的优越性。PAAL在减少约50%至80%标注成本的同时，达到了与全标注数据相当的精度，展现出显著的临床应用潜力。代码公开于 https://github.com/shijun18/PAAL-MedSeg。