Cardiovascular　diseases　are　a　leading　cause　of　mortality　worldwide,　necessitating　accurate　segmentation　of　cardiac　structures　from　3D　CT　and　MR　images　for　effective　diagnosis　and　treatment　planning.　Manual　segmentation　is　time-consuming,　labor-intensive,　and　challenging　due　to　the　complexity　of　cardiac　anatomy　structures.　General　CNNs,　while　effective,　often　struggle　to　generalize　across　different　imaging　domains.　To　address　these　issues,　we　developed　a　PPM-UNet,　which　integrates　a　probabilistic　perturbation　module　(PPM)　into　the　traditional　U-Net　architecture.　Here,　the　PPM　is　introduced　to　control　randomness　during　training,　enhancing　the　model’s　robustness　and　improving　its　performance　across　different　imaging　domains.　Furthermore,　the　PPM　is　lightweight　and　easy　to　implement,　making　it　a　practical　addition　to　existing　network　architectures　for　domain　generalization　tasks　in　medical　imaging.　Evaluated　on　the　WHS++　dataset,　our　method　demonstrates　superior　performance　in　segmenting　cardiac　structures,　particularly　in　unseen　domains,　as　evidenced　by　improved　Dice　Similarity　Coefficient,　Precision,　Sensitivity,　and　Hausdorff　Distance　metrics.　In　addition　to　its　performance　benefits,　the　proposed　module　requires　minimal　computational　overhead,　making　it　practical　for　real-world　clinical　applications.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Switzerland　AG　2025.