Active learning algorithms have become increasingly popular for training models with limited data. However, selecting data for annotation remains a challenging problem due to the limited information available on unseen data. To address this issue, we propose EdgeAL, which utilizes the edge information of unseen images as {\it a priori} information for measuring uncertainty. The uncertainty is quantified by analyzing the divergence and entropy in model predictions across edges. This measure is then used to select superpixels for annotation. We demonstrate the effectiveness of EdgeAL on multi-class Optical Coherence Tomography (OCT) segmentation tasks, where we achieved a 99% dice score while reducing the annotation label cost to 12%, 2.3%, and 3%, respectively, on three publicly available datasets (Duke, AROI, and UMN). The source code is available at \url{https://github.com/Mak-Ta-Reque/EdgeAL}

翻译：主动学习算法在有限数据训练模型中日益普及。然而，由于对未见数据的信息获取有限，选择待标注数据仍是具有挑战性的问题。为解决该问题，我们提出EdgeAL方法，它利用未见图像的边缘信息作为不确定性测量的先验知识。通过分析模型在边缘处预测的散度和熵来量化不确定性，并据此选择待标注的超像素。我们在多类光学相干断层扫描（OCT）分割任务上验证了EdgeAL的有效性：在三个公开数据集（Duke、AROI和UMN）上，该方法分别以12%、2.3%和3%的标注标签成本实现了99%的Dice分数。源代码发布于\url{https://github.com/Mak-Ta-Reque/EdgeAL}