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. Additionally, we also highlight active learning works that are specifically tailored to medical image analysis. 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，导致对大规模专家标注的医学图像数据集的需求日益增长。然而，医学图像标注的高昂成本严重制约了深度学习在该领域的发展。为降低标注成本，主动学习旨在选择最具信息量的样本进行标注，并利用尽可能少的标注样本训练高性能模型。本综述回顾了主动学习的核心方法，包括信息量评估和采样策略。我们首次详细总结了主动学习与其他标签高效技术（如半监督学习、自监督学习等）的融合方式。此外，还重点介绍了专门针对医学图像分析设计的主动学习工作。最后，本文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见解。