Deep learning has achieved widespread success in medical image analysis, leading to an increasing demand for large-scale expert-annotated medical image datasets. Yet, the high cost of annotating medical images severely hampers the development of deep learning in this field. To reduce annotation costs, active learning aims to select the most informative samples for annotation and train high-performance models with as few labeled samples as possible. In this survey, we review the core methods of active learning, including the evaluation of informativeness and sampling strategy. For the first time, we provide a detailed summary of the integration of active learning with other label-efficient techniques, such as semi-supervised, self-supervised learning, and so on. We also summarize active learning works that are specifically tailored to medical image analysis. Additionally, we conduct a thorough comparative analysis of the performance of different AL methods in medical image analysis with experiments. In the end, we offer our perspectives on the future trends and challenges of active learning and its applications in medical image analysis.

翻译：深度学习在医学图像分析领域取得了广泛成功，进而催生了对大规模专家标注医学图像数据集的需求。然而，医学图像标注的高昂成本严重阻碍了深度学习在该领域的发展。为降低标注成本，主动学习旨在选取最具信息量的样本进行标注，从而在尽可能少的标注样本条件下训练高性能模型。本综述中，我们梳理了主动学习的核心方法，包括信息量评估与采样策略。我们首次详细总结了主动学习与其他标签高效技术的融合，例如半监督学习、自监督学习等。同时，我们归纳了专门针对医学图像分析优化的主动学习研究。此外，我们通过实验对不同主动学习方法在医学图像分析中的性能进行了全面对比分析。最后，我们针对主动学习及其在医学图像分析应用中的未来趋势与挑战提出了展望。