In this paper, we explore a principal method to enhance image segmentation quality through limited user interaction. We propose a model solution called the multiscale feature cascading network (MFC-Net), which effectively leverages annotated information and enhances segmentation performance in complex scenes. First, we convert the user-provided click information into a disk map, using two different disk radii to capture interaction influences within different ranges. Then, we employ a dual-channel attention module via multiscale feature cascading. Finally, we devise a refinement module to improve the segmentation results. We validated the effectiveness of MFC-Net on four commonly used image segmentation datasets. Extensive experiments show that MFC-Net could better perceive user’s intentions and significantly reduce the burden of user interaction.