Skin cancer is one of the types of cancer that requires an early diagnosis. The segmentation task plays a vital role in computer-aided diagnosis. Segmenting dermoscopic images is challenging for existing methods due to different image conditions. There is a significant variation in color, texture, shape, size, and location in dermoscopic images. Still, they may contain images with lighting variation and various artifacts, such as hair, ruler, air/oil bubbles, and color sample. The Convolutional Neural Network (CNN) model, UNet, is widely used to segment dermoscopic images. This work proposes a model based on the U-Net architecture to segment dermoscopic images. Still, it presents an ablation study to justify the modifications made in the architecture, such as the number of training epochs, image size, optimization functions, dropout, and the number of convolutional blocks. Experiments were carried out on the ISIC 2017 and ISIC 2018 datasets and show that it is possible to arrive at a simple model capable of presenting competitive results compared to other state-of-the-art works with the appropriate adjustments to their parameters.