Action segmentation supports intention estimation and execution monitoring in assisted teleoperation, but robot demonstrations are costly to collect. We propose an uncertainty-aware framework that transfers temporal knowledge from human to robot demonstrations through MS-TCN++~\cite{li2020mstcnpp} pretraining and fine-tuning, reliability-aware sequence correction, and class-conditional conformal prediction. Across 22 toy-car assembly action classes, fine-tuning with 9 robot demonstrations improves test Edit score from 55.23 to 72.51 and segmental F1@50 from 10.97 to 16.86. The reliability analyzer identifies incorrect segments with 0.755 AUROC. Unrestricted correction reduces Edit to 67.86, while selective acceptance maintains 73.45, primarily safeguarding against harmful relabeling. Conformal prediction achieves 95.38\% empirical coverage at a 93\% target, but prediction sets average 17.41 of 22 classes. These findings support human temporal pretraining while revealing persistent challenges in reliable correction and informative uncertainty quantification with limited robot supervision.

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