The presicion is driven by the random errors, and accuracy is defined by systematic errors. The precision often can be increased by repeated trials increasing the sample size. Accuracy cannot be fixed by collecting more data of the same measurement because systematic error won't go away.
Systematic error leads to bias of the mean and cannot be determined or fixed within the same experiment. Consider this: the whole point of your experiment is often in detecting the effect, such as deviation from zero. You measure the significance by comparing the deviation to the standard error, but that deviation may itself be a bias (systematic error)! That's why the systematic error is the worst kind of error in physical science.
In physics, for instance, you're supposed to determine the bias (systematic error) outside your experiment, then correct for it in your measurements. Interestingly, in economic forecasting field the biggest problem is the shifts of the mean, which basically an equivalent of systematic error or bias in physical sciences.
You may remember how much embarrassment the systematic error caused to the OPERA guys who "detected" neutrinos moving faster than light! They didn't account for a bunch of sources of systematic errors, and had to rescind the conclusion. After all, neutrino do not breach the speed of light, bummer!