In deep learning (DL)-based multifocus image fusion, effective multiscale feature learning is a key issue to promote fusion performance. In this article, we propose a novel DL model named multiscale feature interactive network (MSFIN), which can segment the source images into focused and defocused regions accurately by sufficient interaction of multiscale features from layers of different depths in the network for multifocus image fusion. Specifically, based on the popular encoder–decoder framework, two functional modules, namely, multiscale feature fusion (MSFF) and coordinate attention upsample (CAU), are designed for interactive multiscale feature learning. Moreover, the weighted binary cross-entropy (WBCE) loss and the multilevel supervision (MLS) strategy are introduced to train the network more effectively. Qualitative and quantitative comparisons with 19 representative multifocus image fusion methods demonstrate that the proposed method can achieve state-of-the-art performance. The code of our method is available at https://github.com/yuliu316316/MSFIN-Fusion.