  The adoption of Deep Learning algorithms in the medical imaging field is a
prominent area of research, with high potential for advancing AI-based
Computer-aided diagnosis (AI-CAD) solutions. However, current solutions face
challenges due to a lack of interpretability features and high data demands,
prompting recent efforts to address these issues. In this study, we propose the
ProtoAL method, where we integrate an interpretable DL model into the Deep
Active Learning (DAL) framework. This approach aims to address both challenges
by focusing on the medical imaging context and utilizing an inherently
interpretable model based on prototypes. We evaluated ProtoAL on the Messidor
dataset, achieving an area under the precision-recall curve of 0.79 while
utilizing only 76.54\% of the available labeled data. These capabilities can
enhances the practical usability of a DL model in the medical field, providing
a means of trust calibration in domain experts and a suitable solution for
learning in the data scarcity context often found.
