This paper introduces a novel shallow 3D self-supervised tensor neural network for volumetric segmentation of medical images with merits of obviating training and supervision. The proposed network is referred to as 3D Quantum-inspired Self-supervised Tensor Neural Network (3D-QNet). The underlying architecture of 3D-QNet is composed of a trinity of volumetric layers viz. input, intermediate and output layers inter-connected using an S-connected third-order neighborhood-based topology for voxel-wise processing of 3D medical image data suitable for semantic segmentation. Each of the volumetric layers contains quantum neurons designated by qubits or quantum bits. The incorporation of tensor decomposition in quantum formalism leads to faster convergence of the network operations to preclude the inherent slow convergence problems faced by the classical supervised and self-supervised networks. The segmented volumes are obtained once the network converges. The suggested 3D-QNet is tailored and tested on the BRATS 2019 Brain MR image data set and Liver Tumor Segmentation Challenge (LiTS17) data set extensively in our experiments. 3D-QNet has achieved promising dice similarity as compared to the intensively supervised convolutional network-based models like 3D-UNet, Vox-ResNet, DRINet, and 3D-ESPNet, showing a potential advantage of our self-supervised shallow network on facilitating semantic segmentation.