Computed Tomography （CT） and Magnetic Resonance Imaging （MRI） images are widely used in medical image depth segmentation. However， the traditional segmentation methods are affected by tumor boundary blurring and structural complexity， and ignore adversarial samples’ differentiation ability to the segmentation model， leading to challenges in achieving optimal segmentation results. To address these issues， a 3D medical image segmentation model with adversarial sample-embedded attention U-Net learning was proposed. In the model， by using the adversarial sample-embedded attention U-Net， adversarial samples were constructed through sample transformation， and tumor feature information was extracted from medical images； low-dimensional feature screening and high-dimensional feature fusion modules were introduced to purify the tumor distinguishable feature； the entire network was trained using a combined loss function based on cross-entropy， Dice loss， and contrastive loss to obtain a segmentation model rich in discriminative features. Experimental results show that on Nerve Sheath Tumor （NST） and Automated Cardiac Diagnosis Challenge （ACDC） datasets， the Dice Similarity Coefficients （DSCs） of the proposed method reach 88.14% and 91.75%， respectively， which are improved by 1.26 and 2.48 percentage points compared to those of not new U-Net （nnU-Net） method. It can be seen that the proposed method improves performance of 3D medical image segmentation with blurred tumor boundary effectively.