Correct, bias is the difference between the estimated and true value of a parameter. In study design and analyses there are lots of other types of bias (e.g., measurement error, missing data, etc.), but the statistical definition is as mentioned. Just picture the bull's-eye example, where the estimate is either missing the target or not.
Hlsmith be careful with your wording there. It isn't the difference between the estimated value and the truth. If that was the case then any "unbiased" estimator would always get it exactly right. It's the difference between the expected value of the estimator and the true value.