ObjectiveTo address the inter-observer annotation variability in echocardiographic image segmentation caused by image boundary ambiguity and differences in experts’ clinical backgrounds. MethodsWe proposed an uncertainty-aware consensus segmentation framework based on a conditional diffusion model. Conditioned on the input image, the framework employed a probabilistic consensus strategy to dynamically integrate annotations from multiple experts, jointly modeling stable anatomical consensus and clinically plausible annotation diversity. To compensate for the lack of real multi-expert annotations in publicly available datasets (e.g., CAMUS), we constructed a synthetic multi-expert annotation system using morphological operations to emulate three representative clinical labeling styles including conservative, moderate, and aggressive, providing a reliable foundation for method validation. ResultsThe proposed model significantly outperformed state-of-the-art methods in both left ventricular endocardium (LVEndo) and left ventricular epicardium (LVEpi) segmentation tasks. For LVEndo, the Generalized Energy Distance (\begin{document}$ GED{_{5}}{_{0}} $\end{document}) at the end-diastolic (ED) phase reached 0.073 1,representing a 43.9% reduction compared to the D-Persona model. For LVEpi, the \begin{document}$ GED{_{5}}{_{0}} $\end{document} decreased by 42.0% and 39.0% at the ED and end-systolic phases, respectively, relative to D-Persona. Furthermore, the structural fidelity improved by 2.7-3.5 percentage points for LVEndo and 1.4-2.2 percentage points for LVEpi compared to single-expert models, indicating a superior ability to capture diverse expert preferences. Conclusion By jointly modeling population-level consensus and annotation diversity, this work enables a unified characterization of anatomical structures and their associated annotation uncertainty in cardiac ultrasound images, offering a novel approach toward robust and interpretable segmentation in clinical settings.