Deep neural networks are responsible for great progress in performance for several perceptual tasks, especially in the fields of computer vision, speech recognition, and natural language processing. These results produced a paradigm shift in pattern recognition techniques, shifting the demand from feature extractor design to neural architecture design. However, designing novel deep neural network architectures is very time-consuming and heavily relies on experts intuition, knowledge, and a trial and error process. In that context, the idea of automating the architecture design of deep neural networks has gained popularity, establishing the field of neural architecture search (NAS). To tackle the problem of NAS, authors have proposed several approaches regarding the search space definition, algorithms for the search strategy, and techniques to mitigate the resource consumption of those algorithms. Q-NAS (Quantum-inspired Neural Architecture Search) is one proposed approach to address the NAS problem using a quantum-inspired evolutionary algorithm as the search strategy. That method has been successfully applied to image classification, outperforming handcrafted models on the CIFAR-10 and CIFAR-100 datasets and also on a real-world seismic application. Motivated by this success, we propose SegQNAS (Quantum-inspired Neural Architecture Search applied to Semantic Segmentation), which is an adaptation of Q-NAS applied to semantic segmentation. We carried out several experiments to verify the applicability of SegQNAS on two datasets from the Medical Segmentation Decathlon challenge. SegQNAS was able to achieve a 0.9583 dice similarity coefficient on the spleen dataset, outperforming traditional architectures like U-Net and ResU-Net and comparable results with a similar NAS work from the literature but with fewer parameters network. On the prostate dataset, SegQNAS achieved a 0.6887 dice similarity coefficient, also outperforming U-Net, ResU-Net, and outperforming a similar NAS work from the literature.