It's not that accuracy will always be sacrificed if one wants an explainable model. The point is: if interpretability is an important constraint, it could prevent improvements on accuracy.

Sometimes, the best interpretable model is as good as a black box, and that's great.

When this is not the case, the trade-off is that one should see what's more important for the actual problem. Perhaps interpretability is not a big deal.

Another solution is to try to extract interpretability from the more accurate black box model with something like SHAP.