Using another person's guess as an IV In Estimates of the Economic Return to Schooling from a New Sample of Twins by Orley Ashenfelter and Alan Krueger, they correct sampling error with an IV. They claim that their results imply a larger effect of education on earnings than what was found before.

Each respondent has a twin. Each respondent is asked how much money they make, which has inherent sampling error. Then, the respondent's twin is asked how much money the first respondent makes. That estimate is used as an IV, and together they reveal the true income of the first respondent.

Can this work for revealing how many pennies are in a jar? Or how much a person likes a political candidate?

Or how does this work? I see this as magic. If I want to get the truth about something, I just have to get a second opinion?
There's no magic. What you have to realize is that the result is conditional on the validity of the assumptions:

A) Under the assumption that there is measurement error, then yes, the average of two measurements will be on average closer to the truth than a single opinion. This is very believable. We all do this kind of thing all the time. For example, when we actually get 'a second opinion' from a doctor, we think that adding another opinion gets us closer to the truth. 

B) Under the assumption that there is a smaller incentive to lie to the interviewer about your twin's education than about yours, then one way to get closer to the truth is to use the twin's information about his sibling's education rather than the self-reported one. The authors show that the variances and covariances among the different variables are consistent with this hypothesis. The intuition is that you are less likely to try to hide your sibling's lack of education than you are to hide your own lack of education.

Of course that doesn't make the preferred interpretation by the authors necessarily true. It could still be the case that there is a particular bias in the reporting of your twin's education which they did not consider.  Say for example that you exaggerate the difference between your education and his or hers exactly when there is a large wage difference. That could break their result I think.

Lastly, the precise idea of using it as an instrument is not that it is a second opinion.  Instead, the assumption is that the 'error'  in this second measurement is uncorrelated with income in contrast to the error of the self reported income which is assumed to be correlated with income.  This means that the income effects of education estimated using the sibling report is unbiased, while the one using self-reported income is biased. The authors claim the self-reported one leads to a coefficient that is downward biased, resulting in an estimate of a smaller return to education than the real one.