With the growth in cancer cases and the increasing expenditures in the healthcare system, it is necessary to automate processes, aiming for a faster diagnostic and decrease in expenses. Although current technologies enable to capture high-resolution 3D images of organs, manual segmentation of organs and tumours is still a complex process that requires high expertise. State-of-the-art algorithms are already very accurate. However, they are very compute-intensive tasks, leading to the need for expensive hardware and energy wasting. Coupling state-of-the-art efficient feature extraction algorithms to the nnUNet segmentation framework, this work proposes novel efficient architectures for medical image segmentation. For some tasks, similar results were achieved using around 30% less Floating Point Operations (FLOPs) than the baseline nnUNet, also decreasing the inference time. Morevover, a better performance then nnUNet was achieved using architectures with slightly longer inference time.