Medical image analysis requires substantial labeled data for model training, yet expert annotation is expensive and time-consuming. Active learning (AL) addresses this challenge by strategically selecting the most informative samples for the annotation purpose, but traditional methods solely rely on predictive uncertainty while ignoring whether models learn from clinically meaningful features a critical requirement for clinical deployment. We propose an explainability-guided active learning framework that integrates spatial attention alignment into a sample acquisition process. Our approach advocates for a dual-criterion selection strategy combining: (i) classification uncertainty to identify informative examples, and (ii) attention misalignment with radiologist-defined regions-of-interest (ROIs) to target samples where the model focuses on incorrect features. By measuring misalignment between Grad-CAM attention maps and expert annotations using Dice similarity, our acquisition function judiciously identifies samples that enhance both predictive performance and spatial interpretability. We evaluate the framework using three expert-annotated medical imaging datasets, namely, BraTS (MRI brain tumors), VinDr-CXR (chest X-rays), and SIIM-COVID-19 (chest X-rays). Using only 570 strategically selected samples, our explainability-guided approach consistently outperforms random sampling across all the datasets, achieving 77.22% accuracy on BraTS, 52.37% on VinDr-CXR, and 52.66% on SIIM-COVID. Grad-CAM visualizations confirm that the models trained by our dual-criterion selection focus on diagnostically relevant regions, demonstrating that incorporating explanation guidance into sample acquisition yields superior data efficiency while maintaining clinical interpretability.

翻译：医学影像分析需要大量标注数据进行模型训练，但专家标注成本高昂且耗时。主动学习（AL）通过策略性地选择信息量最大的样本进行标注以应对这一挑战，然而传统方法仅依赖预测不确定性，忽略了模型是否从具有临床意义的特征中学习——这是临床部署的关键要求。我们提出了一种可解释性引导的主动学习框架，将空间注意力对齐整合到样本获取过程中。我们的方法倡导采用双准则选择策略，结合：（i）分类不确定性以识别信息丰富的样本，以及（ii）注意力与放射科医生定义的感兴趣区域（ROI）之间的错位，以锁定模型关注错误特征的样本。通过使用Dice相似度度量Grad-CAM注意力图与专家标注之间的错位，我们的获取函数能够明智地识别出同时提升预测性能和空间可解释性的样本。我们在三个专家标注的医学影像数据集上评估该框架，即BraTS（MRI脑肿瘤）、VinDr-CXR（胸部X光）和SIIM-COVID-19（胸部X光）。仅使用570个策略性选择的样本，我们的可解释性引导方法在所有数据集上均持续优于随机采样，在BraTS上达到77.22%的准确率，在VinDr-CXR上达到52.37%，在SIIM-COVID上达到52.66%。Grad-CAM可视化结果证实，通过我们双准则选择训练的模型能够聚焦于诊断相关区域，这表明将解释性引导纳入样本获取过程能够在保持临床可解释性的同时，实现更优的数据效率。