  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}
