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The defect detection of printed circuit board (PCB) images faces challenges such as limited sample number, imbalanced sample types, and varying detection reliability. To address these issues, this article proposes an uncertainty-aware unsupervised detection model on PCB images, short for $\rm { U^{2}D^{2}}$ PCB. The proposed method uses two U-Net networks to serve as the reconstructive subnetwork and the discriminative subnetwork, respectively. The former one reconstructs defect-free PCB images from defective PCB images, while the latter segments the defects and evaluates the defects uncertainty with the concatenated inputs of the defective and reconstructed images. The $\mathbf { U^{2}D^{2}}$ PCB model is trained in an unsupervised manner with only defect-free images embedding with multiscale artificial defects. Experimental results on the public PCB defect dataset and DeepPCB dataset demonstrate the effectiveness of the proposed method. The mean average precision (mAP) is 99.29% on the PCB defect dataset, while it reaches 95.78% on the DeepPCB dataset. These results are competitive to those of state-of-the-art (SOTA) fully supervised methods. The findings of $\mathrm { U^{2}D^{2}}$ PCB highlight the potential significance of using unsupervised learning techniques for PCB defect detection.