## 5 Discussion
Conversely, when borders are blurred, annotators tend to make more errors in delineation. A model can learn to estimate uncertainty based on these local relationships between boundary clarity and delineation errors. Multiple annotations are not necessary to capture this correspondence. Local examples of discrepancies between object boundaries and manual delineations are sufficient, and these can be gathered from a single annotator's work. Therefore, as our study suggests, averaging multiple annotations has only a marginal effect on improving uncertainty estimation. When comparing ensemble models with MC dropout versions on the NMI metric, ensemble models generally outperform MC models except for the SIJ dataset, where performance is equal. For the GDC and NRMSE metrics, the performance difference between the two models is usually negligible. Ensemble models also consistently outperform regression models on all datasets for the NMI metric. However, GDC and NRMSE performance varies by dataset. For instance, the ensemble model outperforms the regression model on the Knee dataset but performs similarly on the Heart dataset for GDC, with the regression model showing a wider IQR. The regression model slightly outperforms the ensemble model on the Heart dataset for NRMSE. The study also examines the impact of dropout rates on MC models. Performance differences across various dropout rates are minimal, except for the 80% dropout rate, which shows slightly lower performance. Occasionally, specific dropout rates yield better results for certain datasets. For example, the 60% dropout model achieves the best NMI and GDC scores on the Heart dataset, while the 40% dropout model produces the highest GDC score on the Knee dataset. The limited impact of the dropout rate may suggest that the model's weights are organized in such a way that critical information is distributed across multiple neurons or connections. This redundancy allows the model to preserve key information even when some neurons are randomly 'dropped out' during training. Furthermore, this study used three metrics to evaluate model performance: GDC, NMI, and NRMSE. The conclusions drawn from these metrics vary by dataset and model. Sometimes, the metrics agree, such as with the Knee dataset, where the regression model performs the worst across all three metrics, while the SA model and MC with 40% dropout achieve the highest scores. In contrast, the Lung dataset shows significant disagreement between the metrics. All models score a median GDC above 90%, but all score below 1.4 on the NMI metric. The NRMSE metric aligns more with GDC, though ensemble models show performance decline and numerous outliers. Another key finding is that GDC scores are consistently higher than NMI and NRMSE. For example, in the Heart dataset, all modelsachieve GDC scores near 97%, but none surpass an NMI score of 1.7 or an NRMSE score lower than 0.1. Additionally, the analysis revealed potential biases within the metrics. The SIJ dataset shows outstanding NRMSE scores, yet poor performance on GDC and NMI metrics. Similarly, the NMI metric tends to favor the MA model in most cases, suggesting a bias towards this model. Computational complexity is an important consideration for any model, especially those with high resource demands, such as ensemble models or MC dropout. In our case, this complexity is minimized by using a well-known framework like nnU-Net, which automatically optimizes the model's hyperparameters and adapts to the data. Additionally, the experiments were run on a high-performance system with ample RAM and GPU resources. As a result, the model's inference time is very fast. For example, the dropout model takes approximately 0.2 and 0.16 s to segment a single sample from the Lung and SIJ datasets, respectively. Similar results are observed with other models, such as the ensemble model, which requires around 0.13 and 0.25 s to segment a single sample from the Knee and Heart datasets, respectively.