Table 2.

Quantitative prediction and uncertainty estimation performance of the various frameworks on the CamVid, CityScapes, and CT-Organ datasets.

Data (Model)	Uncertainty Estimation Method	Prediction Performance (%)			Uncertainty metrics AUC (%)
		Pixel accuracy	Mean accuracy	Mean IOU	$R_{\mathit{iu}}$	$R_{\mathit{cc}}$	UA
CamVid (SegNet)	None	79.46	65.03	46.31	–	–	–
	MC-Dropout	80.99	65.46	47.31	17.23	82.48	80.18
	MC-DropConnect	82.92	67.47	49.53	21.63	86.54	82.78
CityScapes (ENet)	None	87.50	55.30	44.08	–	–	–
	MC-Dropout	87.38	56.35	44.11	6.12	88.67	84.89
	MC-DropConnect	88.87	63.83	50.25	9.61	90.33	85.57
CT-Organ (VNet)	None	95.19	96.44	65.49	–	–	–
	MC-Dropout	94.11	97.73	67.07	10.81	86.41	91.51
	MC-DropConnect	97.90	97.71	72.77	6.69	87.03	92.59

Our quantitative analyses support the superior performance of the MC-DropConnect in terms of both segmentation accuracy and uncertainty estimation quality.

The models with the best performances are shown in bold.