<p>Image segmentation is inherently challenging because it is often difficult to automatically identify the object of interest in an image. To mitigate this, human-provided markers can be incorporated into the segmentation process, greatly improving accuracy. However, human interaction is an expensive resource and methods that reduce effort in interactive segmentation are of great interest. In this work, we introduce a novel marker-based segmentation layer for deep neural networks, enabling end-to-end training of a marker creation network. Our training methodology includes a loss function with two main components: (i) segmentation loss using the new differentiable segmentation layer and (ii) a set of regularization functions that ensure the generated markers have the desired shape properties. We show that by using the proposed method, the network can automatically generate markers that achieve effective segmentation and have desirable shape characteristics. We validate our results in the training dataset and in five unseen datasets.</p>