Why do decision trees have low accuracy? It seems to be generally acknowledged that decision trees have low prediction accuracy. Is there a concise explanation for why they have low accuracy?

I've read this so much, I've accepted it to be true, but I realize I don't have any intuition as to why it's true.

As an example, here's an excerpt from Elements of Statistical Learning (page 352): 


  Trees have one aspect that prevents them from being the ideal tool for predictive learning, namely inaccuracy. They seldom provide predictive ac- curacy comparable to the best that can be achieved with the data at hand.


Or on Wikipedia, under the heading Disadvantages of Decision Trees: "They are often relatively inaccurate. Many other predictors perform better with similar data. "
It's not true in general. Decision trees tends to overfit in comparison to other algorithms, which provide too low accuracy. But if you use a decision tree in the right way i.e you prepare data in the proper format, use feature selection and perform k-fold cross-validation everything should be ok.

I am sure that you misread it. There is no reason why DT could be a much worse algorithm compared to others.