Image segmentation is the task of extracting the region of interest in images and is one of the main applications of computer vision in the medical domain. Like other computer vision tasks, deep learning is the main solution to image segmentation problems. Deep learning methods are data-hungry and need a huge amount of data for training. On the other side, data shortage is always a problem, especially in the medical domain. Multi-task learning is a technique which helps the deep model to learn better representation from data distribution by introducing related auxiliary tasks. In this study, we investigate a research question to whether it is better to provide this auxiliary information as an input to the network, or is it better to use this task and design a multi-output network. Our findings suggest that however, the multi-output manner improves the overall performance, but the best result achieves when this extra information serves as auxiliary input information.