4D Flow MRI enables full-field quantification of various hemodynamic parameters. While the technique faces limitations related to resolution, deep neural networks have shown promise in enhancing resolution post-scan. So far, however, networks have been predominantly data-driven, overlooking the information carried in individual training sets and instead requiring a substantial amount of training data to converge. To address this, advancements within scientific machine learning propose active learning – a concept in which a learning algorithm interactively queries prediction uncertainty during training and selects unlabeled data of high uncertainty to label for further learning. While assessed in non-medical settings in both practice and theory, the implementation for medical imaging remains unexplored. The aim of this study is to as part of PhD based work and collaborative setup to implement active learning for super-resolution MRI, assessing potential in efficient learning.