Yes eg a medical image classifier can outperform the average doctor. This can be verified by having 10 doctors review each image in the test set to establish a true-consensus.

Even when ML is trained with noisy labels, there are techniques to obtain robust models whose accuracy is overall better than the noise-level in each label. One good opensource library for this: https://github.com/cleanlab/cleanlab/

Another way to get ML that outperforms individual data labelers is to have multiple annotators label your data. Crowdlab is an effective method to analyze such data: https://cleanlab.ai/blog/multiannotator/